With over 50 years of prolonged violent internal conflicts, a persistent risk of inter-communal and religious violence, dozens of armed (…)
22. Dezember 2013
More than 6,000 people dead, more than 4 million people displaced, more than 12 million people affected: In the early days of November 2013, the central parts of the Philippines were hit by a tropical cyclone, which would enter into historical records and the minds of people as Typhoon Yolanda (Haiyan) – in the Philippines, the second deadliest typhoon in history; in the world, amongst the strongest typhoons ever recorded. Preliminary estimates state more than six billion US-Dollars in damages. What happens to be a massive humanitarian crisis (extensively covered in the media) is closely interlinked with the topic of statistical data.
For further images and maps see here.
For up-to-date technical background information see here.
The Philippines is a country which experiences several cyclones every year. I do not want to discuss here if the warning and preparation/evacuation before Typhoon Yolanda was sufficient or not. I want to focus on the very often boring aspect of statistical data. Why? Because statistical data before and after disasters – i.e. basically in every situation for any place – is key. Unfortunately, the key authority for keeping statistical data plays not even a minor role in most debates on politics, the state, or governance on any level from global to local. However, it is the bureau, authority, institute, agency, service, or office of statistics, which is the backbone of preparing and responding to disasters. It is the foundation of proper planning and management.
In order to keep up-to-date records, many resources are required – human, financial, and technical. Obtaining data, filing it, and – ideally – also analyzing and providing it for other institutions and stakeholders can only be successfully done, if the corresponding agency/agencies have not only resources but also the mandate (and power) to do so. Statistical data in the form of sole figures and categories might look to the outsider as plain facts. However, data is highly political! So let us turn to a particular example in the case of Typhoon Yolanda.
The currently available data tells us that approximately one million houses were damaged, roughly half of them totally and the other half partially destroyed. Besides the challenge of defining what makes a house “partially” or already “totally” damaged, this figure is related to the broader policy field of housing. Once we have a number for damaged houses, we could further investigate: How many of these houses have been permanent and non-permanent? How many of these houses were formally or informally built? For how many of these houses do official title deeds (still) exist? As we can already see here: These questions – although possibly focused on sole ‘facts’ – bring much political dynamite with them.
When the initial emergency response stage for a disaster leads over into a transitional stage followed by recovery and reconstruction, many more questions come up, which can only be answered by referring to statistical data: What are the financial damages related to the destroyed houses? What are the incurred losses through damaged houses? How much financial and technical assistance will concerned people need to rebuild their houses? Where shall displaced people resettle? What houses need to be built to improve them with regard to facilities and amenities, as well as to make them more resistant to climate change and disaster risks?
All these questions are on the one hand technical and on the other hand political. Additionally, one should never forget that these questions are also and maybe above all related to human lives. And this is the crux when statistical data shows its extreme importance and difficult or sometimes ugly face: It does not really matter if we are looking at situations before or after disasters, since recurring events like in the Philippines underscore that these two situations are eventually not very different from each other. So in a pre-/post-disaster situation, statistical data should guide technical personnel in assessing the situation, analyzing the key issues, and making fact-based (or more likely estimate-based) recommendations to decision-makers. If statistical data is insufficient for whatever reason (not available, incomplete, wrongly collected, biased, etc.), situations have to be assessed by field visits, interviews, and experience. In the case of Typhoon Yolanda we are talking about an area with the size of the UK. While information is reasonably well collected on the lower levels, assessments are still very much dependent on the technical personnel’s experience with similar situations.
Taking the assessment of situations to the next stage of analyzing issues at stake and recommending recovery and reconstruction activities makes the whole exercise even more difficult and dependent on statistical data. How many? How much? …Those are the question words which are recurring. And extrapolating from scarce incomplete data sources does not just mean to put down a wrong number. As I said earlier: Every single figure represents human lives. One calculates the required amount of GI sheets incorrectly? Well, that could be 500 families without a roof. Often there is room for adjustments afterwards, but one might have already lost another life when the next storm or ‘just’ a torrential tropical rainfall has occurred. So getting numbers right means a lot. Therefore, we should take very seriously the boring task of collecting, filing, and updating statistical data about the places we live in.
On a more personal note, I encourage readers of this blog article to consider supporting post-Yolanda emergency response and/or recovery and reconstruction efforts. More information can be found e.g. here.
This blog article was first published 19 December 2013 on Places.